Computer vision may be defined as the automation of processes used to construct meaningful descriptions of physical objects from images. Many computer vision applications require the ability to match a test image to a reference image. In general, matching establishes an interpretation of input data, where an interpretation is the correspondence between models represented in the computer and the external world of objects. Examples of such applications include vehicle guidance systems, object recognition, stereo vision, and motion following.
Various matching techniques are used, including those that match parameters of a reference model and those that match geometric shapes. Of the latter techniques, many shape matching techniques are similar, in that they define the best match as the one that minimizes a measure of disagreement.
A recent shape matching technique, designed to reduce computational cost, is "chamfer matching". Chamfer matching compares shapes by calculating distances between curve fragments of the test image and curve fragments of the reference image.
A basic step in the chamfer matching process is obtaining a distance array of a reference image. This distance array is obtained from a feature array, in which image features are extracted by applying edge operators. Each element of the feature array records whether or not a line passes through it. The feature array is then transformed into a distance array, which is an array of numbers representing distances to the nearest feature point.
To match a test image to a reference image, the distance array is used as a look-up table to obtain the distance between each feature point of the test image and the nearest feature point of the reference image. These distance values are summed, and a small sum indicates a good match.
Although chamfer matching has been successful in reducing computational cost, the process of obtaining and matching edges requires numerous point calculations along each edge. A need exists for a method of matching images that uses less computational time and fewer resources.